arxiv: 2604.11496 · v2 · submitted 2026-04-13 · 💻 cs.CV · cs.CL· cs.LG

Recognition: unknown

Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference

Imanol Miranda , Ander Salaberria , Eneko Agirre , Gorka Azkune

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:34 UTC · model grok-4.3

classification 💻 cs.CV cs.CLcs.LG

keywords dual-encoder vision-language modelscompositionalityinference protocollocalized alignmentCLIPcompositional generalizationdistribution shiftfine-grained alignment

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The pith

The standard global cosine similarity inference is the main bottleneck for compositional generalization in dual-encoder vision-language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Dual-encoder VLMs such as CLIP perform poorly on compositional tasks because they match entire image and text embeddings rather than aligning their parts. Controlled experiments show that enforcing fine-grained region-to-segment matching at inference time, without any encoder updates, sharply raises scores on compositional benchmarks. A lightweight transformer trained to produce these alignments from frozen patch and token features matches the in-domain retrieval accuracy of full fine-tuning while delivering larger gains on out-of-domain compositional tests. The results indicate that the choice of inference protocol, not the quality of the pretrained representations, determines whether these models can handle novel combinations of objects and relations.

Core claim

Global embedding matching limits compositional ability; replacing it at inference with explicit or learned localized alignment between image regions and text tokens, using only frozen encoders, produces in-domain retrieval performance comparable to full fine-tuning and substantially better out-of-domain compositional generalization than either full fine-tuning or prior end-to-end compositional training methods.

What carries the argument

A lightweight transformer that learns localized region-segment alignments directly from the frozen patch and token embeddings of a dual-encoder VLM, replacing the standard global cosine similarity at inference time.

If this is right

Localized alignment over frozen representations matches full fine-tuning on in-domain retrieval tasks.
Localized alignment yields larger improvements than full fine-tuning on controlled out-of-domain compositional benchmarks.
Global embedding matching constitutes a key bottleneck preventing robust compositional generalization in dual-encoder VLMs.
Alignment mechanisms rather than end-to-end retraining are sufficient for strong compositional generalization under distribution shift.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

VLM architectures could add small inference-time alignment modules instead of retraining the entire model for each new domain.
Diagnostic protocols that separate representation quality from inference protocol could be applied to other reported VLM limitations such as bias or robustness failures.
If the lightweight transformer generalizes across base models, similar localized matching might improve compositionality in other multimodal dual-encoder systems.

Load-bearing premise

The controlled diagnostic experiments and out-of-domain benchmarks truly isolate the effect of the inference protocol without confounding factors such as dataset biases or unintended cues.

What would settle it

A controlled test in which full fine-tuning produces equal or larger gains than the localized-alignment method on the same out-of-domain compositional benchmarks would show that the inference protocol is not the primary bottleneck.

Figures

Figures reproduced from arXiv: 2604.11496 by Ander Salaberria, Eneko Agirre, Gorka Azkune, Imanol Miranda.

**Figure 1.** Figure 1: Vision-language compositional reasoning requires fine-grained alignment between textual segments describing [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Examples from the BISCOR-CTRL dataset. From left to right: instances from the COLOR, SIZE, MATERIAL, and QUANTITY categories (the last containing 8 objects). Each instance consists of two image–caption pairs: a correct pair (top image and caption) and a hard negative pair (bottom image and caption). of image evidence [Lin et al., 2024b,a, Miranda et al., 2024, Udandarao et al., 2025]. Accordingly, BISCOR-C… view at source ↗

**Figure 3.** Figure 3: Example of a BISCOR instance after loading the dataset. Maintenance plan We are committed to maintaining the dataset to resolve any technical issues. We actively track issues in the HuggingFace or GitHub repositories. Licensing Our work is licensed under the MIT License4 for the code and a Creative Commons Attribution 4.0 International License (CC BY 4.0) for the data5 . Author statement We, the authors, a… view at source ↗

**Figure 4.** Figure 4: An example for our two text segmenting strategies. As can be seen, [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

read the original abstract

Dual-encoder Vision-Language Models (VLMs) such as CLIP are often characterized as bag-of-words systems due to their poor performance on compositional benchmarks. We argue that this limitation may stem less from deficient representations than from the standard inference protocol based on global cosine similarity. First, through controlled diagnostic experiments, we show that explicitly enforcing fine-grained region-segment alignment at inference dramatically improves compositional performance without updating pretrained encoders. We then introduce a lightweight transformer that learns such alignments directly from frozen patch and token embeddings. Comparing against full fine-tuning and prior end-to-end compositional training methods, we find that although these approaches improve in-domain retrieval, their gains do not consistently transfer under distribution shift. In contrast, learning localized alignment over frozen representations matches full fine-tuning on in-domain retrieval while yielding substantial improvements on controlled out-of-domain compositional benchmarks. These results identify global embedding matching as a key bottleneck in dual-encoder VLMs and highlight the importance of alignment mechanisms for robust compositional generalization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows that a lightweight inference-time transformer on frozen dual-encoder embeddings can match full fine-tuning in-domain while improving compositional robustness out-of-domain.

read the letter

The main point is that global cosine similarity at inference, not the embeddings themselves, is the bigger bottleneck for compositionality in models like CLIP. The authors demonstrate this by first running diagnostics that enforce region-to-segment alignment at test time and get clear gains with no weight updates. They then train a small transformer on the frozen patch and token features to learn those alignments directly. This setup matches full fine-tuning on standard retrieval while delivering stronger results on their out-of-domain compositional tests, where full fine-tuning and prior end-to-end methods fall short under shift. That contrast is the useful new piece: it suggests you can improve generalization without sacrificing the original model or paying the full retraining cost. The work is empirical and stays grounded in direct comparisons rather than new theory. The diagnostics are a clean way to isolate the inference step, and the OOD focus addresses a real practical concern for deployed VLMs. The soft spot is whether the controlled out-of-domain benchmarks fully separate compositionality from other dataset regularities. If those sets still contain co-occurrence patterns or spatial shortcuts that the localized matcher can use but global similarity cannot, the attribution to inference protocol weakens. The abstract claims the experiments control for this, but the strength of the conclusion rests on how thoroughly that was checked. Readers working on efficient robustness fixes for existing VLMs will get the most from it. The idea is straightforward enough that practitioners could try the lightweight adapter quickly. It deserves peer review because the empirical pattern is clear enough to test and the practical implication is worth referee scrutiny even if the benchmarks need tighter validation.

Referee Report

1 major / 1 minor

Summary. The paper claims that compositional limitations in dual-encoder VLMs such as CLIP stem primarily from the global cosine similarity inference protocol rather than from the pretrained representations. Diagnostic experiments show that enforcing explicit region-segment alignment at inference improves compositional performance without updating encoders. A lightweight transformer is introduced to learn localized alignments from frozen patch and token embeddings; this matches full fine-tuning on in-domain retrieval while delivering substantial gains on controlled out-of-domain compositional benchmarks, identifying global embedding matching as the key bottleneck.

Significance. If the results hold, the work would meaningfully shift VLM research from representation-centric fine-tuning toward inference-time alignment mechanisms. The lightweight transformer offers an efficient path to compositional robustness that preserves in-domain performance, and the empirical contrast with end-to-end methods provides a practical demonstration that global matching is a removable limitation rather than an inherent representational deficit.

major comments (1)

[Experimental evaluation and OOD benchmark definitions] The central claim that gains on controlled OOD compositional benchmarks reflect the inference protocol (rather than exploitation of benchmark-specific statistics) is load-bearing for the contrast with full fine-tuning. The manuscript must demonstrate that these OOD sets lack unintended correlations such as object co-occurrence frequencies, spatial priors, or attribute-visual shortcuts that localized alignment could exploit while global cosine similarity cannot; without explicit bias audits or controls in the experimental section, attribution to the alignment mechanism remains insecure.

minor comments (1)

[Abstract] The abstract would be strengthened by briefly naming the specific OOD compositional benchmarks and reporting the magnitude of the observed improvements (e.g., percentage gains or absolute scores) to allow readers to assess the practical significance immediately.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which identifies a key requirement for strengthening the attribution of our results to the inference protocol. We address the major comment below and outline the revisions we will make.

read point-by-point responses

Referee: The central claim that gains on controlled OOD compositional benchmarks reflect the inference protocol (rather than exploitation of benchmark-specific statistics) is load-bearing for the contrast with full fine-tuning. The manuscript must demonstrate that these OOD sets lack unintended correlations such as object co-occurrence frequencies, spatial priors, or attribute-visual shortcuts that localized alignment could exploit while global cosine similarity cannot; without explicit bias audits or controls in the experimental section, attribution to the alignment mechanism remains insecure.

Authors: We agree that explicit bias audits are necessary to secure the attribution of gains to the localized alignment mechanism. Section 4.2 of the manuscript details the construction of the OOD benchmarks, which are built from standard datasets using held-out combinations of objects, attributes, and relations to enforce compositional novelty. To directly address the concern, we will add quantitative bias audits in the revised experimental section. These will include: (i) co-occurrence frequency matrices for object pairs and attribute-object combinations, (ii) spatial prior statistics (e.g., bounding-box centroid distributions), and (iii) attribute-visual shortcut correlations, all compared between in-domain and OOD splits. We will also report whether localized alignment shows differential exploitation of any residual correlations relative to global cosine similarity. This addition will make the controls explicit and allow readers to evaluate the security of our claims. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical evaluation of inference protocols

full rationale

The paper advances an empirical hypothesis that compositional failures in dual-encoder VLMs stem primarily from global cosine-similarity inference rather than encoder representations. This is tested via controlled diagnostic experiments that enforce region-segment alignment at inference time on frozen encoders, followed by introduction of a lightweight transformer trained on those frozen patch/token embeddings. Results are compared against full fine-tuning and prior end-to-end methods on both in-domain retrieval and out-of-domain compositional benchmarks. No equations, self-definitional loops, fitted-input predictions, or load-bearing self-citations appear in the derivation; all performance deltas are measured against external baselines and distribution-shift controls. The central claim therefore rests on falsifiable experimental contrasts rather than any reduction of outputs to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on the abstract alone, the paper relies on standard machine-learning assumptions about benchmark validity and transformer capacity to learn alignments; no explicit free parameters, domain axioms, or invented entities are described.

pith-pipeline@v0.9.0 · 5482 in / 1105 out tokens · 35853 ms · 2026-05-10T16:34:13.336509+00:00 · methodology

discussion (0)

Reference graph

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and (112, 56), combining different scales and aspect ratios. We resize all the crops to the input size of the model and we deploy those crops in two different ways: i)grid, avoiding any overlap of crops of the same size, and ii)overlap, using a stride of crop_size/2. This means that we process 86 crops per image with thegridconfiguration, and 270 crops wi...

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E.4 Software information ModelsWe detail the sources of models we used. • CLIP:We obtain the pretrained baseline VIT-B-32 OpenAI’s CLIP model [Radford et al., 2021] from Hugging Face12. •SigLIP 2:We obtain all SigLIP 2 [Tschannen et al., 2025] models from Hugging Face collection

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– Pe:We obtain PE-Core-B16-224 from the official Hugging Face repository

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19 •TripletCLIP:We obtain the TripletCLIP model [Patel et al., 2024] from the official GitHub repository

•NegCLIP:We obtain the NegCLIP model [Yuksekgonul et al., 2022] from the official GitHub repository. 19 •TripletCLIP:We obtain the TripletCLIP model [Patel et al., 2024] from the official GitHub repository. 20 •FSC-CLIP:We obtain the FSC-CLIP model [Oh et al., 2024] from the official GitHub repository. 21 •FineCLIP:We obtain the FineCLIP model [Jing et al...

2022